Clicking on any phenotype will show you an overview of the GWAS results:

• Detailed info about phenotype definition

• Manhattan plot

• List of top hits

• Q-Q-plot

Manhattan plot

Clicking on any point will lead you to the.

Top hits

• Cromwell-29 and 31

• Wdltool-0.14

• Plink 1.9 and 2.0

• BCFtools 1.5 and 1.7

• Eagle 2.3.5

• Beagle 4.1 (version 08Jun17.d8b)

• R 3.4.1 (packages: data.table 1.10.4, sm 2.2-5.4)

Chip genotype data processing and QC Samples were genotyped with Illumina (Illumina Inc., San Diego, CA, USA) and Affymetrix arrays (Thermo Fisher Scientific, Santa Clara, CA, USA).

Genotype calls were made with GenCall and zCall algorithms for Illumina and AxiomGT1 algorithm for Affymetrix data.

Chip genotyping data produced with previous chip platforms and reference genome builds were lifted over to build version 38 (GRCh38/hg38) following the protocol described here: dx.doi.org/10.17504/protocols.io.nqtddwn.

Quality control

In sample-wise quality control, individuals with ambiguous gender, high genotype missingness (>5%), excess heterozygosity (+-4SD) and non-Finnish ancestry were excluded. In variant-wise quality control variants with high missingness (>2%), low HWE P-value (<1e-6) and minor allele count, MAC<3 were excluded.

Pre-phasing

Prior imputation, chip genotyped samples were pre-phased with Eagle 2.3.5 () with the default parameters, except the number of conditioning haplotypes was set to 20,000.

This is a description of the quality control procedures applied before running the GWAS.

In summary, we removed 4,095 samples who were either of non-Finnish ancestry or twins/duplicates. Finnish ancestry was assessed with a combination of PCA and a Bayesian method for outlier detection.

Sample QC

Our data set initially consists of 102,739 samples, of which we kept 100,355 after removing duplicates. Next, we proceeded to exclude samples of non-Finnish ancestry using a PCA approach.

PCA

After filtering for high quality HQ variants (36,073 variants) we merged the data set with the (EUR individuals only). At this point we performed a PCA on the merged data set and used a Bayesian approach to determine outliers (see below). This process allowed us to identify samples from outside the Central/Northern European region (1,023 samples). Western European and British samples are still present, but are not enough to drive a signal in the PCA. Thus we used a different approach; we ran a PCA on the 99,333 samples left and we projected the 98 Finnish (FIN) and 89 non-Finnish European (EUR) samples from the thousand genomes project who survived round one onto the same space. Then, for each Finngen sample, we calculate its Mahalanobis distance to the FIN and EUR centroid. The distance is mapped to a probability with a distribution with 3 degrees of freedom. Then, we define as being Finns, those sample for whom the relative probability of being Finnish vs European is > 95%. This left us with 98,644 samples.

Missing Data

Of the 98,644 non-duplicate PCA inliers, we removed 2,145 individuals that didn’t have phenotype or age data. Thus the final number of analyzed individuals was ​96,499​​.

Further info

Bayesian outlier detection

Code for the method can be found here:​.

Documentation from the original developers of the algorithm can be found here: .

Centroid based outlier detection

The Figure below shows how the centroid based outlier detection works by plotting the distribution of the first 3 components of the PCA. We can see that the FinnGen samples labelled as Western European (in blue) are extremely close to the Western European centroid in the first two components.

Purple and green dots represent samples of Finnish and Western European (EUR) respectively from the thousand genome data set. The blue dots are FinnGen samples who have been found to be more likely to belong to the EUR group rather than to the Finnish one. Dots in red on the other hand are labelled as belonging to the Finnish centroid.

To download FinnGen summary statistics you will need to fill the online form at . You will then receive an email containing the detailed instructions for downloading the data.

Using FinnGen data for publications

Please remember to acknowledge the FinnGen study when using these results in publications.

You can use the following text:

We want to acknowledge the participants and investigators of FinnGen study

FinnGen a public-private partnership project combining genotype data from Finnish biobanks and digital health record data from Finnish health registries. FinnGen provides a unique opportunity to study genetic variation in relation to disease trajectories in an isolated population.

FinnGen is a growing project, aiming at 500,000 individuals in 2023.

FinnGen results are subjected to one year embargo and, after that, available to the larger scientific community via the or through .

For matters related to this documentation, click Edit on GitHubor send us an email to finngen-info@helsinki.fi.

Please consider visiting the study website: https://www.finngen.fi/en and follow FinnGen on twitter: @FinnGen_FI

If you want to host FinnGen summary statistics on your website, please get in contact with us at: humgen-servicedesk@helsinki.fi.

Please use the following description when referring to our project:

The FinnGen study is a large-scale genomics initiative that has analyzed over 500,000 Finnish biobank samples and correlated genetic variation with health data to understand disease mechanisms and predispositions. The project is a collaboration between research organisations and biobanks within Finland and international industry partners.

When using these results in publications, please remember to:

1. Acknowledge the FinnGen study. You can use the following text:

“We want to acknowledge the participants and investigators of the FinnGen study”

Timeline for releases:

• FinnGen association locus zoom plot

• Annotation with GWAS catalog variants + UK Biobank hits

• ClinVar annotation

Details

• URL locus zoom: http://r2.finngen.fi/region/endpoint/CHR:START-END, e.g. (CHR: chromosome on hg38,START/END: window start and end position on hg38)

• For chromosome X, use either X or 23.

Contains all endpoints/phenotypes for which a GWAS was run (if more than 100 cases).

The variant view has the following URL: http://r2.finngen.fi/variant/CHR-POS-ALT-REF, e.g.

• CHR: chromosome on hg38 (1-22, X or 23)

• POS: position on hg38

Clicking on any gene will bring you to the gene view with association results for that gene region, the loss-of-function analysis results (for methods see ) and an annotated list of all loss of function and missense variants.

LoF burden results

The disease endpoints were defined using nationwide registries:

• Drug purchase and Drug Reimbursement

• Digital and Population Data Services Agency

• Statistics Finland

We harmonized over the International Classification of Diseases (ICD) revisions 8, 9 and 10, cancer-specific ICD-O-3, (NOMESCO) procedure codes, Finnish-specific Social Insurance Institute (KELA) drug reimbursement codes and ATC-codes.

These registries spanning decades were electronically linked to the cohort baseline data using the unique national personal identification numbers assigned to all Finnish citizens and residents.

A full list of FinnGen endpoints is for release 2.

The endpoints with fewer than 100 cases, near-duplicate endpoints, and developmental “helper” endpoints were excluded from the final PheWas (column “OMIT”).

Endpoints with N<150 are not released by (Finnish Institute for Health and Welfare).

The web browser r2.finngen.fi contains all FinnGen GWAS results from release 2 and provides you with three options:

1. Search for the GWAS result of a variant, phenotype or gene.

2. Explore the loss-of-function burden (LoF) for gene-phenotypes combinations.

3. Find a particular phenotype/endpoint.

We used the SAIGE software for running the R2 GWAS.

SAIGE is a mixed model logistic regression R/C++ package, able to account for related samples.

We analyzed:

• ​1,122 endpoints

• 96,499 samples

• 17,054,975 variants

We included the following covariates in the model: sex, age, 10 PCs, genotyping batch.

The following biobanks and cohorts are part of the R2 release:

FinnGen individuals were with Illumina and Affymetrix chip arrays (Illumina Inc., San Diego, and Thermo Fisher Scientific, Santa Clara, CA, USA).

Chip genotype data were using the population-specific of 3,775 whole genomes.

Post-imputation QC involved excluding variants with imputation INFO < 0.7.

• Total number of individuals: 102,739

• Total number of variants (merged set): 17,054,975

v3 consists of 3,775 high coverage (30x) WGS Finnish individuals from six cohorts:

1. METSIM (PIs Markku Laakso and Mike Boehnke)

2. FINRISK (PI Pekka Jousilahti)

3. Health2000 (PI Seppo Koskinen)

Genotype imputation was done with the population-specific .

Variant call set was produced with GATK HaplotypeCaller algorithm by following GATK best-practices for variant calling.

Genotype-, sample- and variant-wise QC was applied in an iterative manner by using the and the resulting high-quality WGS data for 3,775 individuals were phased with Eagle 2.3.5 as described in the previous section.

Genotype imputation was carried out by using the population-specific SISu v3 imputation reference panel with (version 08Jun17.d8b) as described in the following protocol: .

Post-imputation quality-control involved checking expected conformity of the imputation INFO-value distribution, MAF differences between the target dataset and the imputation reference panel and checking chromosomal continuity of the imputed genotype calls.

Optional: Post-imputation quality control also involved excluding variants imputed with imputation INFO<0.7.

Null models

For the null model calculation for each endpoint, we used age, sex, 10 PCs and genotyping batch as covariates.

For calculating the genetic relationship matrix, we used 49,811 independent, common, well-imputed variants with a posterior genotyping probability >0.95 and missingness <0.05 (LD r2 < 0.1, MAF > 0.05, INFO > 0.95).

SAIGE options for the null computation:

• LOCO = false

• numMarkers = 30

• traceCVcutoff = 0.0025

• ratioCVcutoff = 0.001

Association tests

We ran association tests against each of the 1,122 endpoints with for each variant with a minimum allele count of 10 from the imputation pipeline (SAIGE optionminMAC = 10). The alternative allele is always the effect allele.

Software

The code we used is available in . The original SAIGE codebase is available in .

We estimated the loss of function (LoF) burden of each gene on every endpoint.

First, we calculated per individual and gene whether any loss of function variant(s) was present, yielding a $n \times p$ matrix with 0 and 1 values ( $n$being the number of individuals and $p$ the number of genes).

Then we used the new summarised variables as input in the SAIGE GWAS, replacing the genotype matrix that was used in the regular GWAS.

We ran the analysis in Google Cloud using WDL and Cromwell. The WDL workflow metadata including SAIGE commands and their inputs are available at:

gs://finngen-production-library-green/R2/workflows